In [10]:
image = random.choice(index)
Image(filename=f'/content/drive/MyDrive/workspace/Yolov5/new/data/training_images/{image}.jpg',width=600)
Out[10]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
%cd /content/drive/MyDrive/workspace/Yolov5/new
/content/drive/MyDrive/workspace/Yolov5/new
# !mkdir car
%cd car
/content/drive/MyDrive/workspace/Yolov5/new/car
import os, time, random
import numpy as np
import pandas as pd
import cv2, torch
from tqdm.auto import tqdm
import shutil as sh
from IPython.display import Image, clear_output
import matplotlib.pyplot as plt
%matplotlib inline
%%time
# !git clone https://github.com/ultralytics/yolov5 # clone repo
!pip install -U pycocotools
!pip install -qr yolov5/requirements.txt # install dependencies
!cp yolov5/requirements.txt ./
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (2.0.6)
Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pycocotools) (3.7.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pycocotools) (1.22.4)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.0.7)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (4.39.3)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.4.4)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (8.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (2.8.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=2.1.0->pycocotools) (1.16.0)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 184.3/184.3 kB 4.8 MB/s eta 0:00:00
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CPU times: user 106 ms, sys: 11.5 ms, total: 117 ms
Wall time: 10.7 s
img_h, img_w, num_channels = (380, 676, 3)
df = pd.read_csv('/content/drive/MyDrive/workspace/Yolov5/new/data/train_solution_bounding_boxes (1).csv')
df.rename(columns={'image':'image_id'}, inplace=True)
df['image_id'] = df['image_id'].apply(lambda x: x.split('.')[0])
print(df.head())
image_id xmin ymin xmax ymax 0 vid_4_1000 281.259045 187.035071 327.727931 223.225547 1 vid_4_10000 15.163531 187.035071 120.329957 236.430180 2 vid_4_10040 239.192475 176.764801 361.968162 236.430180 3 vid_4_10020 496.483358 172.363256 630.020260 231.539575 4 vid_4_10060 16.630970 186.546010 132.558611 238.386422
df['x_center'] = (df['xmin'] + df['xmax'])/2
df['y_center'] = (df['ymin'] + df['ymax'])/2
df['w'] = df['xmax'] - df['xmin']
df['h'] = df['ymax'] - df['ymin']
df['classes'] = 0
df['x_center'] = df['x_center']/img_w
df['w'] = df['w']/img_w
df['y_center'] = df['y_center']/img_h
df['h'] = df['h']/img_h
df.head()
| image_id | xmin | ymin | xmax | ymax | x_center | y_center | w | h | classes | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | vid_4_1000 | 281.259045 | 187.035071 | 327.727931 | 223.225547 | 0.450434 | 0.539817 | 0.068741 | 0.095238 | 0 |
| 1 | vid_4_10000 | 15.163531 | 187.035071 | 120.329957 | 236.430180 | 0.100217 | 0.557191 | 0.155572 | 0.129987 | 0 |
| 2 | vid_4_10040 | 239.192475 | 176.764801 | 361.968162 | 236.430180 | 0.444645 | 0.543678 | 0.181621 | 0.157014 | 0 |
| 3 | vid_4_10020 | 496.483358 | 172.363256 | 630.020260 | 231.539575 | 0.833213 | 0.531451 | 0.197540 | 0.155727 | 0 |
| 4 | vid_4_10060 | 16.630970 | 186.546010 | 132.558611 | 238.386422 | 0.110347 | 0.559122 | 0.171491 | 0.136422 | 0 |
index = list(set(df.image_id))
image = random.choice(index)
print("Image ID: %s"%(image))
img = cv2.imread(f'/content/drive/MyDrive/workspace/Yolov5/new/data/training_images/{image}.jpg')
img.shape
Image ID: vid_4_21600
(380, 676, 3)
image = random.choice(index)
Image(filename=f'/content/drive/MyDrive/workspace/Yolov5/new/data/training_images/{image}.jpg',width=600)
import os
import shutil as sh
from tqdm import tqdm
source = 'training_images'
# os.makedirs('/data')
folds = [0] # Define the list of fold values here
for fold in folds:
# os.makedirs('/data/fold{}'.format(fold))
val_index = index[len(index)*fold//5:len(index)*(fold+1)//5]
for name, mini in tqdm(df.groupby('image_id')):
if name in val_index:
path2save = 'val2017/'
else:
path2save = 'train2017/'
if not os.path.exists('data/fold{}/labels/'.format(fold)+path2save):
os.makedirs('data/fold{}/labels/'.format(fold)+path2save)
with open('data/fold{}/labels/'.format(fold)+path2save+name+".txt", 'w+') as f:
row = mini[['classes','x_center','y_center','w','h']].astype(float).values
row = row.astype(str)
for j in range(len(row)):
text = ' '.join(row[j])
f.write(text)
f.write("\n")
if not os.path.exists('data/fold{}/images/{}'.format(fold,path2save)):
os.makedirs('data/fold{}/images/{}'.format(fold,path2save))
sh.copy("/content/drive/MyDrive/workspace/Yolov5/new/data/{}/{}.jpg".format(source,name),'data/fold{}/images/{}/{}.jpg'.format(fold,path2save,name))
100%|██████████| 355/355 [02:45<00:00, 2.15it/s]
!ls data/fold0/images/train2017
vid_4_10000.jpg vid_4_16180.jpg vid_4_21540.jpg vid_4_600.jpg vid_4_1000.jpg vid_4_16280.jpg vid_4_21560.jpg vid_4_6160.jpg vid_4_10020.jpg vid_4_16300.jpg vid_4_21580.jpg vid_4_6180.jpg vid_4_10040.jpg vid_4_16320.jpg vid_4_21600.jpg vid_4_6200.jpg vid_4_10060.jpg vid_4_16400.jpg vid_4_2160.jpg vid_4_6220.jpg vid_4_10100.jpg vid_4_16420.jpg vid_4_21620.jpg vid_4_6240.jpg vid_4_10120.jpg vid_4_16500.jpg vid_4_21640.jpg vid_4_6260.jpg vid_4_10140.jpg vid_4_16660.jpg vid_4_21660.jpg vid_4_6280.jpg vid_4_1020.jpg vid_4_16680.jpg vid_4_21680.jpg vid_4_6300.jpg vid_4_1040.jpg vid_4_16700.jpg vid_4_2180.jpg vid_4_6320.jpg vid_4_10480.jpg vid_4_16720.jpg vid_4_2200.jpg vid_4_6340.jpg vid_4_10500.jpg vid_4_17040.jpg vid_4_22220.jpg vid_4_6360.jpg vid_4_10520.jpg vid_4_17060.jpg vid_4_22240.jpg vid_4_6380.jpg vid_4_1060.jpg vid_4_17080.jpg vid_4_22540.jpg vid_4_6400.jpg vid_4_10960.jpg vid_4_17100.jpg vid_4_22560.jpg vid_4_6420.jpg vid_4_10980.jpg vid_4_17120.jpg vid_4_22580.jpg vid_4_6440.jpg vid_4_11000.jpg vid_4_17140.jpg vid_4_22640.jpg vid_4_6480.jpg vid_4_11020.jpg vid_4_17160.jpg vid_4_22660.jpg vid_4_6500.jpg vid_4_11240.jpg vid_4_17180.jpg vid_4_22700.jpg vid_4_6520.jpg vid_4_11260.jpg vid_4_17240.jpg vid_4_22720.jpg vid_4_680.jpg vid_4_11280.jpg vid_4_17260.jpg vid_4_22740.jpg vid_4_700.jpg vid_4_11380.jpg vid_4_17320.jpg vid_4_22980.jpg vid_4_740.jpg vid_4_11400.jpg vid_4_17340.jpg vid_4_23000.jpg vid_4_8220.jpg vid_4_11420.jpg vid_4_17360.jpg vid_4_2380.jpg vid_4_8240.jpg vid_4_11440.jpg vid_4_17400.jpg vid_4_2400.jpg vid_4_8260.jpg vid_4_11880.jpg vid_4_17420.jpg vid_4_2420.jpg vid_4_8280.jpg vid_4_11900.jpg vid_4_17440.jpg vid_4_24760.jpg vid_4_8300.jpg vid_4_11920.jpg vid_4_17540.jpg vid_4_2540.jpg vid_4_8320.jpg vid_4_11940.jpg vid_4_17560.jpg vid_4_26300.jpg vid_4_8340.jpg vid_4_11960.jpg vid_4_17580.jpg vid_4_26320.jpg vid_4_8560.jpg vid_4_11980.jpg vid_4_17600.jpg vid_4_26340.jpg vid_4_8580.jpg vid_4_12000.jpg vid_4_1760.jpg vid_4_26360.jpg vid_4_8600.jpg vid_4_12040.jpg vid_4_17620.jpg vid_4_26380.jpg vid_4_860.jpg vid_4_12060.jpg vid_4_17640.jpg vid_4_26400.jpg vid_4_8640.jpg vid_4_12080.jpg vid_4_17660.jpg vid_4_26420.jpg vid_4_8660.jpg vid_4_12100.jpg vid_4_17680.jpg vid_4_26440.jpg vid_4_8680.jpg vid_4_12120.jpg vid_4_1780.jpg vid_4_26460.jpg vid_4_8700.jpg vid_4_12140.jpg vid_4_1800.jpg vid_4_26480.jpg vid_4_8720.jpg vid_4_12160.jpg vid_4_18180.jpg vid_4_26500.jpg vid_4_8740.jpg vid_4_12180.jpg vid_4_18200.jpg vid_4_26520.jpg vid_4_880.jpg vid_4_12200.jpg vid_4_1820.jpg vid_4_26540.jpg vid_4_8960.jpg vid_4_12220.jpg vid_4_18320.jpg vid_4_26560.jpg vid_4_8980.jpg vid_4_12240.jpg vid_4_18360.jpg vid_4_26580.jpg vid_4_9000.jpg vid_4_12260.jpg vid_4_1840.jpg vid_4_28200.jpg vid_4_900.jpg vid_4_12280.jpg vid_4_1860.jpg vid_4_28220.jpg vid_4_9020.jpg vid_4_12300.jpg vid_4_1880.jpg vid_4_28240.jpg vid_4_9040.jpg vid_4_12320.jpg vid_4_18820.jpg vid_4_28440.jpg vid_4_9060.jpg vid_4_12340.jpg vid_4_18840.jpg vid_4_28820.jpg vid_4_9080.jpg vid_4_12360.jpg vid_4_18860.jpg vid_4_28840.jpg vid_4_9200.jpg vid_4_12380.jpg vid_4_18880.jpg vid_4_28860.jpg vid_4_920.jpg vid_4_12480.jpg vid_4_1900.jpg vid_4_28880.jpg vid_4_9220.jpg vid_4_13580.jpg vid_4_19040.jpg vid_4_29460.jpg vid_4_9240.jpg vid_4_13620.jpg vid_4_19060.jpg vid_4_29480.jpg vid_4_9260.jpg vid_4_13640.jpg vid_4_19080.jpg vid_4_29500.jpg vid_4_9280.jpg vid_4_13660.jpg vid_4_1920.jpg vid_4_29520.jpg vid_4_9300.jpg vid_4_13680.jpg vid_4_1940.jpg vid_4_29540.jpg vid_4_9320.jpg vid_4_13700.jpg vid_4_1960.jpg vid_4_29560.jpg vid_4_9340.jpg vid_4_13740.jpg vid_4_19740.jpg vid_4_29880.jpg vid_4_940.jpg vid_4_13760.jpg vid_4_19760.jpg vid_4_29900.jpg vid_4_9420.jpg vid_4_13780.jpg vid_4_19780.jpg vid_4_29920.jpg vid_4_9440.jpg vid_4_13800.jpg vid_4_1980.jpg vid_4_29940.jpg vid_4_9460.jpg vid_4_13820.jpg vid_4_19880.jpg vid_4_29960.jpg vid_4_9500.jpg vid_4_13840.jpg vid_4_19900.jpg vid_4_29980.jpg vid_4_9520.jpg vid_4_13860.jpg vid_4_19920.jpg vid_4_30000.jpg vid_4_9540.jpg vid_4_13880.jpg vid_4_2000.jpg vid_4_30020.jpg vid_4_9560.jpg vid_4_13900.jpg vid_4_2020.jpg vid_4_30440.jpg vid_4_9580.jpg vid_4_14140.jpg vid_4_2040.jpg vid_4_3120.jpg vid_4_9600.jpg vid_4_14160.jpg vid_4_2060.jpg vid_4_3140.jpg vid_4_960.jpg vid_4_14340.jpg vid_4_2080.jpg vid_4_3160.jpg vid_4_9640.jpg vid_4_14360.jpg vid_4_2100.jpg vid_4_3180.jpg vid_4_9660.jpg vid_4_14380.jpg vid_4_21160.jpg vid_4_3200.jpg vid_4_9700.jpg vid_4_14400.jpg vid_4_21180.jpg vid_4_3220.jpg vid_4_9720.jpg vid_4_14440.jpg vid_4_2120.jpg vid_4_3240.jpg vid_4_9740.jpg vid_4_14460.jpg vid_4_21220.jpg vid_4_3260.jpg vid_4_9780.jpg vid_4_14480.jpg vid_4_21240.jpg vid_4_3340.jpg vid_4_9800.jpg vid_4_14500.jpg vid_4_21260.jpg vid_4_3360.jpg vid_4_980.jpg vid_4_15000.jpg vid_4_21280.jpg vid_4_3380.jpg vid_4_9820.jpg vid_4_15020.jpg vid_4_21300.jpg vid_4_3460.jpg vid_4_9840.jpg vid_4_15040.jpg vid_4_21320.jpg vid_4_3520.jpg vid_4_9860.jpg vid_4_16000.jpg vid_4_21400.jpg vid_4_3540.jpg vid_4_9880.jpg vid_4_16020.jpg vid_4_2140.jpg vid_4_3560.jpg vid_4_9900.jpg vid_4_16040.jpg vid_4_21420.jpg vid_4_3800.jpg vid_4_9960.jpg vid_4_16060.jpg vid_4_21440.jpg vid_4_3820.jpg vid_4_9980.jpg vid_4_16120.jpg vid_4_21480.jpg vid_4_3840.jpg vid_4_16140.jpg vid_4_21500.jpg vid_4_4520.jpg vid_4_16160.jpg vid_4_21520.jpg vid_4_4540.jpg
%%writefile dataset.yaml
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../data/fold0/ # dataset root dir
train: images/train2017 # train images (relative to 'path') 128 images
val: images/val2017 # val images (relative to 'path') 128 images
test: # test images (optional)
# Classes (80 COCO classes)
names:
0: car
Overwriting dataset.yaml
!python yolov5/train.py --batch 12 --epochs 12 --data dataset.yaml --weights yolov5s.pt --name yolov5x_fold0_new
train: weights=yolov5s.pt, cfg=, data=dataset.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=12, batch_size=12, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=yolov5x_fold0_new, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-163-g016e046 Python-3.10.11 torch-2.0.0+cu118 CPU hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/ Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf... 100% 755k/755k [00:00<00:00, 16.7MB/s] Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 1 1182720 models.common.C3 [512, 512, 1] 9 -1 1 656896 models.common.SPPF [512, 512, 5] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 361984 models.common.C3 [512, 256, 1, False] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 90880 models.common.C3 [256, 128, 1, False] 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs Transferred 343/349 items from yolov5s.pt optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.00046875), 60 bias albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) train: Scanning /content/drive/MyDrive/workspace/Yolov5/new/car/data/fold0/labels/train2017... 341 images, 0 backgrounds, 0 corrupt: 100% 341/341 [00:10<00:00, 31.12it/s] train: New cache created: /content/drive/MyDrive/workspace/Yolov5/new/car/data/fold0/labels/train2017.cache val: Scanning /content/drive/MyDrive/workspace/Yolov5/new/car/data/fold0/labels/val2017... 128 images, 0 backgrounds, 0 corrupt: 100% 128/128 [00:09<00:00, 13.44it/s] val: New cache created: /content/drive/MyDrive/workspace/Yolov5/new/car/data/fold0/labels/val2017.cache AutoAnchor: 4.96 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to yolov5/runs/train/yolov5x_fold0_new2/labels.jpg... Image sizes 640 train, 640 val Using 2 dataloader workers Logging results to yolov5/runs/train/yolov5x_fold0_new2 Starting training for 12 epochs... Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0/11 0G 0.1036 0.033 0 14 640: 100% 29/29 [08:26<00:00, 17.45s/it] Class Images Instances P R mAP50 mAP50-95: 0% 0/6 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 1.700s exceeded Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:45<00:00, 7.53s/it] all 128 201 0.292 0.398 0.364 0.0989 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 1/11 0G 0.07688 0.02839 0 15 640: 100% 29/29 [08:06<00:00, 16.79s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:40<00:00, 6.73s/it] all 128 201 0.64 0.443 0.497 0.181 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 2/11 0G 0.07302 0.02308 0 13 640: 100% 29/29 [08:18<00:00, 17.18s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:38<00:00, 6.44s/it] all 128 201 0.313 0.667 0.289 0.109 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 3/11 0G 0.0698 0.02137 0 23 640: 100% 29/29 [08:09<00:00, 16.86s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:36<00:00, 6.09s/it] all 128 201 0.504 0.781 0.582 0.199 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 4/11 0G 0.0631 0.01999 0 10 640: 100% 29/29 [08:15<00:00, 17.09s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:35<00:00, 5.95s/it] all 128 201 0.93 0.865 0.945 0.507 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 5/11 0G 0.05432 0.0171 0 13 640: 100% 29/29 [08:13<00:00, 17.03s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:35<00:00, 5.88s/it] all 128 201 0.869 0.866 0.88 0.344 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 6/11 0G 0.04788 0.0167 0 14 640: 100% 29/29 [08:05<00:00, 16.75s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:35<00:00, 5.93s/it] all 128 201 0.97 0.966 0.982 0.408 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 7/11 0G 0.04259 0.01465 0 15 640: 100% 29/29 [08:01<00:00, 16.61s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:36<00:00, 6.16s/it] all 128 201 0.949 0.935 0.958 0.422 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 8/11 0G 0.04038 0.01438 0 8 640: 100% 29/29 [08:06<00:00, 16.78s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:34<00:00, 5.78s/it] all 128 201 0.942 0.945 0.973 0.578 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 9/11 0G 0.03532 0.0132 0 17 640: 100% 29/29 [08:12<00:00, 16.97s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:36<00:00, 6.00s/it] all 128 201 0.937 0.965 0.975 0.591 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 10/11 0G 0.03439 0.01324 0 11 640: 100% 29/29 [08:05<00:00, 16.73s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:34<00:00, 5.79s/it] all 128 201 0.97 0.962 0.988 0.564 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 11/11 0G 0.03145 0.01259 0 15 640: 100% 29/29 [08:08<00:00, 16.83s/it] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:35<00:00, 5.87s/it] all 128 201 0.985 0.98 0.992 0.659 12 epochs completed in 1.762 hours. Optimizer stripped from yolov5/runs/train/yolov5x_fold0_new2/weights/last.pt, 14.3MB Optimizer stripped from yolov5/runs/train/yolov5x_fold0_new2/weights/best.pt, 14.3MB Validating yolov5/runs/train/yolov5x_fold0_new2/weights/best.pt... Fusing layers... Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:36<00:00, 6.06s/it] all 128 201 0.985 0.98 0.992 0.66 Results saved to yolov5/runs/train/yolov5x_fold0_new2
!ls yolov5/runs/train/yolov5x_fold0_new/weights/
best.pt last.pt
from PIL import Image
Image.open("yolov5/runs/train/yolov5x_fold0_new2/F1_curve.png")
Image.open("yolov5/runs/train/yolov5x_fold0_new2/PR_curve.png")
!python yolov5/detect.py --weights yolov5/runs/train/yolov5x_fold0_new/weights/best.pt --img 676 --conf 0.6 --source /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images
detect: weights=['yolov5/runs/train/yolov5x_fold0_new/weights/best.pt'], source=/content/drive/MyDrive/workspace/Yolov5/new/data/testing_images, data=yolov5/data/coco128.yaml, imgsz=[676, 676], conf_thres=0.6, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-163-g016e046 Python-3.10.11 torch-2.0.0+cu118 CPU Fusing layers... Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs WARNING ⚠️ --img-size [676, 676] must be multiple of max stride 32, updating to [704, 704] image 1/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25100.jpg: 416x704 (no detections), 275.8ms image 2/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25120.jpg: 416x704 (no detections), 273.7ms image 3/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25140.jpg: 416x704 (no detections), 267.3ms image 4/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25160.jpg: 416x704 (no detections), 269.2ms image 5/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25180.jpg: 416x704 (no detections), 267.6ms image 6/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25200.jpg: 416x704 (no detections), 282.9ms image 7/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25220.jpg: 416x704 (no detections), 273.6ms image 8/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25240.jpg: 416x704 (no detections), 254.4ms image 9/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_25260.jpg: 416x704 (no detections), 268.4ms image 10/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26320.jpg: 416x704 (no detections), 274.1ms image 11/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26400.jpg: 416x704 (no detections), 274.9ms image 12/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26420.jpg: 416x704 (no detections), 260.3ms image 13/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26560.jpg: 416x704 1 car, 257.3ms image 14/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26580.jpg: 416x704 1 car, 272.3ms image 15/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26600.jpg: 416x704 (no detections), 257.5ms image 16/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26620.jpg: 416x704 1 car, 406.1ms image 17/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26640.jpg: 416x704 3 cars, 442.5ms image 18/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26660.jpg: 416x704 1 car, 449.1ms image 19/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26680.jpg: 416x704 2 cars, 435.9ms image 20/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26700.jpg: 416x704 2 cars, 432.5ms image 21/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26720.jpg: 416x704 3 cars, 448.6ms image 22/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26740.jpg: 416x704 3 cars, 432.7ms image 23/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26760.jpg: 416x704 3 cars, 431.7ms image 24/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26780.jpg: 416x704 2 cars, 786.2ms image 25/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26800.jpg: 416x704 3 cars, 669.4ms image 26/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26820.jpg: 416x704 2 cars, 453.2ms image 27/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26840.jpg: 416x704 1 car, 259.1ms image 28/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26860.jpg: 416x704 1 car, 262.7ms image 29/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26880.jpg: 416x704 1 car, 275.2ms image 30/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26900.jpg: 416x704 2 cars, 257.0ms image 31/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26920.jpg: 416x704 2 cars, 287.5ms image 32/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26940.jpg: 416x704 2 cars, 273.8ms image 33/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26960.jpg: 416x704 1 car, 260.9ms image 34/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_26980.jpg: 416x704 (no detections), 261.3ms image 35/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27240.jpg: 416x704 1 car, 266.7ms image 36/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27260.jpg: 416x704 (no detections), 255.6ms image 37/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27280.jpg: 416x704 (no detections), 256.3ms image 38/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27300.jpg: 416x704 (no detections), 280.1ms image 39/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27320.jpg: 416x704 1 car, 282.2ms image 40/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27360.jpg: 416x704 1 car, 263.4ms image 41/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27380.jpg: 416x704 (no detections), 265.2ms image 42/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27400.jpg: 416x704 1 car, 284.3ms image 43/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27420.jpg: 416x704 3 cars, 267.2ms image 44/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27440.jpg: 416x704 3 cars, 262.4ms image 45/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27460.jpg: 416x704 3 cars, 285.6ms image 46/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27480.jpg: 416x704 3 cars, 271.9ms image 47/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27500.jpg: 416x704 1 car, 272.9ms image 48/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27520.jpg: 416x704 1 car, 297.0ms image 49/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27540.jpg: 416x704 (no detections), 271.6ms image 50/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27560.jpg: 416x704 (no detections), 254.8ms image 51/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27580.jpg: 416x704 (no detections), 258.3ms image 52/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27600.jpg: 416x704 (no detections), 296.7ms image 53/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27620.jpg: 416x704 1 car, 265.3ms image 54/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27640.jpg: 416x704 1 car, 268.6ms image 55/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27660.jpg: 416x704 (no detections), 270.8ms image 56/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27680.jpg: 416x704 (no detections), 272.7ms image 57/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27700.jpg: 416x704 (no detections), 262.0ms image 58/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27720.jpg: 416x704 (no detections), 290.2ms image 59/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27740.jpg: 416x704 (no detections), 290.8ms image 60/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27760.jpg: 416x704 (no detections), 414.2ms image 61/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27780.jpg: 416x704 (no detections), 445.8ms image 62/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27800.jpg: 416x704 (no detections), 477.5ms image 63/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27820.jpg: 416x704 (no detections), 450.8ms image 64/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27840.jpg: 416x704 1 car, 436.5ms image 65/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27860.jpg: 416x704 1 car, 466.3ms image 66/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27880.jpg: 416x704 1 car, 459.5ms image 67/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27900.jpg: 416x704 1 car, 458.6ms image 68/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27920.jpg: 416x704 1 car, 464.9ms image 69/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27940.jpg: 416x704 (no detections), 451.8ms image 70/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27960.jpg: 416x704 (no detections), 297.9ms image 71/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_27980.jpg: 416x704 (no detections), 268.9ms image 72/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28000.jpg: 416x704 (no detections), 269.4ms image 73/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28020.jpg: 416x704 (no detections), 268.6ms image 74/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28040.jpg: 416x704 (no detections), 265.5ms image 75/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28060.jpg: 416x704 (no detections), 259.1ms image 76/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28080.jpg: 416x704 (no detections), 296.3ms image 77/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28180.jpg: 416x704 (no detections), 275.8ms image 78/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28260.jpg: 416x704 1 car, 269.4ms image 79/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28320.jpg: 416x704 (no detections), 265.3ms image 80/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28340.jpg: 416x704 (no detections), 270.9ms image 81/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28360.jpg: 416x704 (no detections), 270.9ms image 82/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28380.jpg: 416x704 1 car, 273.8ms image 83/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28420.jpg: 416x704 1 car, 270.4ms image 84/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28440.jpg: 416x704 1 car, 270.7ms image 85/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28460.jpg: 416x704 (no detections), 270.7ms image 86/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28480.jpg: 416x704 (no detections), 273.9ms image 87/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28500.jpg: 416x704 (no detections), 278.5ms image 88/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28520.jpg: 416x704 (no detections), 264.1ms image 89/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28540.jpg: 416x704 (no detections), 276.7ms image 90/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28560.jpg: 416x704 (no detections), 268.5ms image 91/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28580.jpg: 416x704 (no detections), 281.9ms image 92/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28600.jpg: 416x704 (no detections), 274.6ms image 93/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28620.jpg: 416x704 (no detections), 270.1ms image 94/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28640.jpg: 416x704 (no detections), 283.5ms image 95/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28660.jpg: 416x704 (no detections), 274.5ms image 96/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28680.jpg: 416x704 (no detections), 281.7ms image 97/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_28700.jpg: 416x704 (no detections), 275.6ms image 98/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29000.jpg: 416x704 2 cars, 276.6ms image 99/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29020.jpg: 416x704 1 car, 269.0ms image 100/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29040.jpg: 416x704 1 car, 256.8ms image 101/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29060.jpg: 416x704 (no detections), 268.5ms image 102/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29080.jpg: 416x704 (no detections), 273.1ms image 103/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29100.jpg: 416x704 (no detections), 294.5ms image 104/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29400.jpg: 416x704 1 car, 445.0ms image 105/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29420.jpg: 416x704 2 cars, 434.4ms image 106/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29440.jpg: 416x704 2 cars, 445.7ms image 107/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29460.jpg: 416x704 1 car, 451.8ms image 108/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29480.jpg: 416x704 1 car, 439.6ms image 109/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29500.jpg: 416x704 (no detections), 456.5ms image 110/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29520.jpg: 416x704 (no detections), 436.5ms image 111/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29540.jpg: 416x704 2 cars, 465.5ms image 112/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29560.jpg: 416x704 1 car, 419.8ms image 113/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29580.jpg: 416x704 1 car, 447.6ms image 114/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29600.jpg: 416x704 (no detections), 266.1ms image 115/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29620.jpg: 416x704 (no detections), 268.8ms image 116/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29640.jpg: 416x704 (no detections), 318.1ms image 117/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29720.jpg: 416x704 (no detections), 288.7ms image 118/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29740.jpg: 416x704 (no detections), 267.8ms image 119/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29760.jpg: 416x704 1 car, 286.4ms image 120/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29820.jpg: 416x704 1 car, 266.7ms image 121/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29840.jpg: 416x704 1 car, 256.7ms image 122/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29860.jpg: 416x704 (no detections), 270.3ms image 123/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29880.jpg: 416x704 (no detections), 282.9ms image 124/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29900.jpg: 416x704 (no detections), 272.2ms image 125/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_29980.jpg: 416x704 (no detections), 261.8ms image 126/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30000.jpg: 416x704 (no detections), 278.2ms image 127/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30020.jpg: 416x704 (no detections), 254.1ms image 128/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30040.jpg: 416x704 (no detections), 255.1ms image 129/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30120.jpg: 416x704 (no detections), 259.7ms image 130/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30140.jpg: 416x704 (no detections), 274.2ms image 131/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30160.jpg: 416x704 (no detections), 280.5ms image 132/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30180.jpg: 416x704 (no detections), 271.3ms image 133/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30640.jpg: 416x704 1 car, 271.2ms image 134/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30660.jpg: 416x704 (no detections), 265.5ms image 135/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30680.jpg: 416x704 (no detections), 258.0ms image 136/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30700.jpg: 416x704 (no detections), 276.4ms image 137/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30720.jpg: 416x704 (no detections), 294.6ms image 138/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30740.jpg: 416x704 1 car, 260.1ms image 139/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30760.jpg: 416x704 1 car, 264.4ms image 140/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30820.jpg: 416x704 1 car, 274.5ms image 141/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30840.jpg: 416x704 (no detections), 268.7ms image 142/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30860.jpg: 416x704 1 car, 263.3ms image 143/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30920.jpg: 416x704 1 car, 270.7ms image 144/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_30940.jpg: 416x704 1 car, 285.4ms image 145/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31020.jpg: 416x704 2 cars, 266.4ms image 146/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31040.jpg: 416x704 2 cars, 265.1ms image 147/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31060.jpg: 416x704 1 car, 329.0ms image 148/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31080.jpg: 416x704 2 cars, 432.9ms image 149/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31100.jpg: 416x704 2 cars, 447.3ms image 150/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31120.jpg: 416x704 2 cars, 463.4ms image 151/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31140.jpg: 416x704 1 car, 443.4ms image 152/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31160.jpg: 416x704 1 car, 458.0ms image 153/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31180.jpg: 416x704 (no detections), 458.3ms image 154/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31200.jpg: 416x704 (no detections), 444.0ms image 155/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31260.jpg: 416x704 (no detections), 437.7ms image 156/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31280.jpg: 416x704 (no detections), 437.1ms image 157/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31300.jpg: 416x704 (no detections), 416.0ms image 158/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31360.jpg: 416x704 (no detections), 261.8ms image 159/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31380.jpg: 416x704 (no detections), 275.1ms image 160/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31400.jpg: 416x704 (no detections), 276.3ms image 161/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31420.jpg: 416x704 (no detections), 277.4ms image 162/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31480.jpg: 416x704 (no detections), 306.3ms image 163/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31500.jpg: 416x704 (no detections), 267.7ms image 164/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31520.jpg: 416x704 (no detections), 262.2ms image 165/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31560.jpg: 416x704 1 car, 278.2ms image 166/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31600.jpg: 416x704 2 cars, 279.8ms image 167/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31620.jpg: 416x704 1 car, 271.3ms image 168/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31640.jpg: 416x704 (no detections), 272.5ms image 169/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31660.jpg: 416x704 (no detections), 271.6ms image 170/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31680.jpg: 416x704 (no detections), 271.8ms image 171/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31700.jpg: 416x704 1 car, 273.0ms image 172/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_31720.jpg: 416x704 1 car, 264.9ms image 173/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_400.jpg: 416x704 1 car, 279.2ms image 174/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_420.jpg: 416x704 1 car, 264.4ms image 175/175 /content/drive/MyDrive/workspace/Yolov5/new/data/testing_images/vid_5_440.jpg: 416x704 1 car, 266.5ms Speed: 1.0ms pre-process, 316.0ms inference, 0.6ms NMS per image at shape (1, 3, 704, 704) Results saved to yolov5/runs/detect/exp
predicted_files = []
for (dirpath, dirnames, filenames) in os.walk("./yolov5/runs/detect/exp"):
predicted_files.extend(filenames)
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
Image.open(f'./yolov5/runs/detect/exp/{random.choice(predicted_files)}')
